Bibliographic record
Abstract
absorptive capacity in Argentina 204-6 in Canada 56, 216 in Costa Rica 56, 229-31 GM crops 204-5 influences on 16-17 in Jamaica 56 in Malta 56 meaning 18 mobile phones 205-6, 231, 288 in Mozambique 287-9 open source software 205-6, 230-31, 288 plant tissue cultures 205, 230-31, 287 recombinant insulin 204, 230 technology-specific nature of 13-14 trends 56 in United States 56 Advisory National Commission of Agricultural Biotechnology (CONABIA)(Argentina) 209, 213 Alcatel-Lucent 260 América Móvil Claro 128, 206 Angel Gallardo 95 anti-trust policy, influences of 19, 325 Apache 162, 164-5, 260 Apple 129-30 Argentina economic and developmental background 26-9 economic indicators 24, 28 educational trends 28 emerging technologies, generally absorptive capacity 204-6 access and penetration rates 210-12 diffusion generally 212-13 inequalities 207-8 policy and regulation, influence of 206-10 socio-economic influences on 203
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".